KCNA Cloud Native Application Delivery Practice Question
During a canary deployment using Argo Rollouts, how does the tool determine the success of the canary before promoting it?
⚠ Common exam trap
KCNA often tests the mechanism of automated analysis in Argo Rollouts, and candidates may confuse manual approval or status checks with metric-based analysis, missing the role of AnalysisTemplates.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
By analyzing predefined metrics (e.g., error rate) via an AnalysisTemplate
Argo Rollouts uses AnalysisTemplates to define metrics queries (e.g., Prometheus, Datadog) that are evaluated during a canary deployment. The success of the canary is determined by these predefined metrics, such as error rate or latency, against thresholds. If the analysis passes, the rollout is promoted; if it fails, the rollout is aborted or rolled back.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
By checking the rollout's status field in the YAML
Why it's wrong here
Argo Rollouts evaluates canary health through configured analysis templates, which query metrics from Prometheus or other providers; the status field merely reflects the outcome. Reading status YAML is tempting because it displays rollout progress, but it is the result of analysis, not the mechanism determining success.
- ✗
By requiring manual approval via a webhook
Why it's wrong here
Manual approval is an optional pause step, not how Argo Rollouts determines canary success; automated analysis templates evaluate metrics to decide promotion. Manual gates suit regulated releases needing human sign-off, but the question asks how success is measured, which analysis runs provide.
- ✗
By comparing the ReplicaSet's age to a threshold
Why it's wrong here
Argo Rollouts promotes based on analysis runs against metrics, not elapsed time; ReplicaSet age is irrelevant to health evaluation. Age thresholds resemble Kubernetes readiness probes, which gate individual pods, but canary promotion requires measured success criteria rather than a timer.
- ✓
By analyzing predefined metrics (e.g., error rate) via an AnalysisTemplate
Why this is correct
Argo Rollouts queries predefined metrics such as error rate through an AnalysisTemplate, which defines the Prometheus or other provider queries and success thresholds. The controller evaluates these during the canary step and only promotes when results pass, satisfying the stem's requirement for automated success determination before promotion.
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Same concept, more angles
1 more way this is tested on KCNA
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A team uses Argo Rollouts for progressive delivery. They configure a canary rollout with a traffic split of 20% to the new version. After verification, the rollout automatically increases traffic to 100%. Which Argo Rollout manifest field controls this gradual traffic increase?
hard- A.strategy.canary.trafficRouting
- ✓ B.strategy.canary.steps
- C.template.spec.containers
- D.spec.replicas
Why B: In an Argo Rollouts canary strategy, the strategy.canary.steps field defines the ordered list of steps, including setWeight (traffic percentage), pause, and analysis. The gradual traffic increase from 20% to 100% is controlled by the sequence of setWeight steps defined in strategy.canary.steps.
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CNCF exam blueprint
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